HFC: Towards an Effective Model for the Improvement of heart Diagnosis with Clustering Techniques

Heart disease pretends great danger to people, as heart disease has recently become a dangerous disease that acts as a threat to humans. It usually affects all groups from young to old. The biggest challenge in this paper is data pre-processing and discovering a solution to the failure of records Clinical heart, where an effective high-performance model is proposed to enhance heart disease and treat failure in the clinical heart failure records. The current authors applied the techniques of clustering with k-means, expectation-maximization clustering, DBSCAN, support vector clustering, and random clustering herein. Using cluster techniques, we gained good enough results for significantly predicting and improving the performance of heart disease. The goal of the model is a suggestion of a reduction method to find features of heart disease by applying several techniques. Our most important results are to predict faster and better. It indicates that the proposed model is excellent and gives excellent results. This model demonstrated a great superiority over its counterparts through the results obtained in this research. We obtained some values of 130, 980, 183, 125.133, 133, 203, and 125.800. It confirms that this model will predict significantly and improve the performance of the data that we have worked on this.